BlaRoq | R Documentation |
Blanchard-Roquain (2008) step-up Procedure for arbitrary dependent p-Values Also proposed independently by Sarkar (2008)
BlaRoq(pValues, alpha, pii, silent=FALSE)
pValues |
pValues to be used. They can have arbitrary dependence. |
alpha |
the level at which the FDR should be controlled |
pii |
Prior for the proportion of true null hypotheses, same size as pValues |
silent |
if true any output on the console will be suppressed. |
A generalization of the Benjamini-Yekutieli procedure, taking as an additional parameter a distribution pii on [1..k] (k is the number of hypotheses) representing prior belief on the number of hypotheses that will be rejected.
It is a step-up Procedure with critical values C_i defined as alpha/k times the sum for j in [1..i] of j*pii[j]. For any fixed prior pii, the FDR is controlled at level alpha for arbitrary dependence structure of the p-Values. The particular case of the Benjamini-Yekutieli step-up is recovered by taking pii[i] proportional to 1/i.
If pii is missing, a default prior distribution proportional to exp( -i/(0.15*k) ) is taken. It should perform better than the BY procedure if more than about 0.05 to 0.1 of hypotheses are rejected, and worse otherwise.
Note: the procedure automatically normalizes the prior pii to sum to one if this is not the case.
A list containing:
adjPValues |
A numeric vector containing the adjusted pValues |
rejected |
A logical vector indicating which hypotheses are rejected |
criticalValues |
A numeric vector containing critical values used in the step-up test |
errorControl |
A Mutoss S4 class of type |
GillesBlanchard,HackNiklas
Blanchard, G. and Roquain, E. (2008). Two simple sufficient conditions for FDR control. Electronic Journal of Statistics, 2:963-992. Sarkar, S.K. (2008) On methods controlling the false discovery rate. Sankhya, Series A, 70:135-168.
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